Automatic seismic pattern recognition method

ABSTRACT

An automatic seismic pattern recognition method includes the steps of: determining a given number of seismic patterns to be recognized; providing a set of seismic trace portions for the region; defining a pattern recognition parameter common to all the trace portions, and determining the value of the parameters for each of the traces portions of the set. The method also includes the steps of: selecting trace portions of the set; selecting a one-dimensional neural network containing as many cells as there are patterns to be recognized where each cell is assigned a value of the recognition parameter; and submitting the neural network to a learning process with the selected trace portions so that at the end of the process each cell matches a pattern to be recognized and so that the patterns are progressively ordered. The method also includes the steps of: presenting each trace portion of the set to be processed to the classified and ordered neural network and attributing to each trace portion presented to the network the number of the cell closest to it.

BACKGROUND OF THE INVENTION

The present invention relates to a method for automatic recognition ofseismic facies between two horizons, or about a horizon of a geologicalarea, or, more particularly between two horizons or about a horizondefined on a seismic section associated with said geological area.

At present, nearly all geological and geophysical interpretationsrelative to seismic facies are carried out on an interpretation stationand belong to the specialized domain of seismic stratigraphy.

In seismic stratigraphy, it is customary to identify and to represent ona map the variations of seismic facies in a given slice of thegeological area to be surveyed (called mapping). The slice may or maynot be between two marked horizons.

A seismic facies unit is a group of seismic reflections havingconfigurations, i.e., external shape and internal parameters, which aredifferent from one facies unit to another. The configurations may alsobe different between two adjacent or consecutive facies units.

The seismic facies units are usually defined by analyzing three familiesof parameters:

the configuration of the reflections, (e.g., parallel, divergent,sigmoid, etc.),

the external shape (e.g., concave upwards, convex upwards, draped,etc.),

the internal parameters of the reflections (e.g., amplitude, frequency,etc.).

The recognition of the seismic facies in a given geological area is veryimportant because it provides useful information, particularly about thetypes of sedimentary deposits and the anticipated lithology.

To succeed in recognizing the seismic facies of a given geological area,it is necessary to define each of them first by separately analyzing atleast each of the above-mentioned three families of parameters. Next,the parameters should be synthesized in order to gather the maximum dataor information about the seismic facies present in the geological area.

The cost of such an analysis and the means to be employed, particularlythe data processing means, are excessively high as compared to theresults obtained.

In fact, if the seismic facies which one wishes to recognize belong tostratigraphic pinchouts and/or to turbiditic channels, it is verydifficult to discriminate between the anomalies when they appear on theusual seismic sections, even if those anomalies are recognized by thewell seismic survey as being present in the area concerned, providedthat a well is available in the area, which may not be the case.

In EP-0 561 492, a method is described for improving the well logging bymaking use of neural networks. The particular network described is alayered network. From a statistical standpoint, a layered network is auniversal approximator of the boundaries between classes, but, above allit is, a supervised network. In other words, the quantity obtained inthe output of the neural network is compared with a quantity known anddetermined by other methods, until a coincidence or quasi-coincidence isobtained between the quantities.

Since the topological maps due to Kohonen are used in other fields,particularly in the medical field to determine models susceptible toimitate a number of the functions of the brain by reproducing some ofits basic structures, geophysicists have attempted to apply them to thefield of geophysics.

Particular applications are described in U.S. Pat. No. 5,373,486, whichdeals with the classification of seismic events by using Kohonenantagonistic networks, in U.S. Pat. No. 5,355,313, which describes theinterpretation of aeromagnetic data, and in U.S. Pat. No. 5,181,171,which describes an interactive neural network adapted to detect thefirst arrivals on the seismic traces.

SUMMARY OF THE INVENTION

It is an object of the present invention to propose a method forrecognizing seismic facies from a seismic section associated with ageological area, and to do this automatically via an unsupervised neuralnetwork.

The present invention relates to a method for recognizing seismic faciesbetween two horizons or about a horizon of a geological area, andcomprises the steps of:

determining a given number of seismic facies to be recognized,

taking a set of seismic trace portions concerning said area,

defining a facies recognition parameter common to all the trace portionsand determining the value of said parameter for each of the traceportions of the set,

selecting trace portions from said set,

choosing a one-dimensional neural network containing as many cells asfacies to be recognized, each cell being assigned a value of therecognition parameter,

effecting the learning of the neural network via the selected traceportions, so that, when the learning process is complete, each cellcorresponds to a facies to be recognized, and so that said facies aregradually ordered,

presenting each trace portion of said set to be processed to the classedand ordered neural network, and

assigning the number of the nearest cell to each of the trace portionspresented to the network.

One advantage of the present invention is that it allows theidentification for example, of a variation of seismic faciescorresponding to a stratigraphic pinchout or to lineaments which can beinterpreted as faults.

According to another feature, the neural network is of the unsupervisedtype and consists in particular of a one-dimensional Kohonen topologicalmap.

According to a further feature, the trace portions comprise the samenumber of samples and the recognition parameter is defined by thesequence of samples comprised between the two horizons or about thehorizon delimited on a seismic section.

According to a further feature, the trace portions are used to determinean overall recognition parameter common to all the trace portions.

According to a further feature, each facies is assigned a color code,the different colors being gradually ordered in a given range of colorswith a slow variation of shade between any two consecutive colors of therange.

According to a further feature, the seismic facies recognized arerepresented on a map with their corresponding color.

A further advantage of the present invention resides in the fact that,in accordance with the general knowledge about the geological area to besurveyed and obtained by other means, it is possible to use attributesassociated with the seismic traces which are derived either from thesequence of samples between the top and the base of the trace element,i.e., between two horizons or about a horizon of said area, or from theoverall statistical parameters which are significant to the distributionof the samples in the geological area concerned. The overall parametersmay be, for example, the amplitude, frequency, interval velocity etc.

The above and other advantages and features of the present inventionwill appear from a reading of the description of the method of theinvention, and from the appended drawings wherein;

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a seismic section on which a turbiditic channel is delimitedabout a marked horizon,

FIG. 2 schematically represents a final topological map obtainedaccording to the invention, and

FIG. 3 schematically represents a map of the seismic facies associatedwith the geological area corresponding to the set of seismic traceportions processed.

DETAILED DESCRIPTION OF THE INVENTION

The method of the present invention uses an unsupervised one-dimensionalneural network which comprises as many cells as seismic facies to berecognized in a given geological area. The seismic facies may be locatedbetween two horizons or about a horizon, and are determined by picking aseismic section which contains a large number of seismic traces, in therange of at least several hundred traces (for example, about horizon Hin FIG. 1). A set of seismic trace portions is delimited, on the traceswhich are bounded by the horizons or about the horizon. These seismictrace portions are used to prepare a topological map in the mannerdescribed below.

According to the method of the invention, a seismic facies recognitionparameter is defined where the parameter is common to all the traceportions. With reference to the example shown in FIG. 2, the parameteris defined by the shape of the signal. There are fifteen signal shapesnumbered from 0 to 14, each one corresponding to a seismic facies.Obviously, two signal shapes may be similar to one another which meansthat any corresponding seismic facies are similar in nature and/or arecontinuous. The value of the recognition parameter is determined, which,in the example of the signal shape, is made up of the sequence ofsamples on the trace portion concerned. Each trace portion is sampled inthe same way (i.e., comprises the same number of samples) while theamplitude of the samples may vary from one sample to another in the samesequence, or in different sequences.

A number of trace portions are selected from the set of trace portionsto be processed, for example, by selecting one trace portion out offour, or by making a random or pseudo-random selection.

In a further step of the invention, the learning of the neural networkis effected via selected trace portions so that, when the learningprocess is complete, each set corresponds to a facies to be recognized.This is shown in FIG. 2, where the fifteen cells numbered from 0 to 14correspond to the given number (fifteen) of facies to be recognized andeach facies is being determined by the shape of the signal shown in oneof the fifteen classes corresponding to the fifteen facies to berecognized. To achieve the dual objective of classifying and orderingthe classes, the following learning process is obtained.

Let E be the set of selected trace portions to be classified, and M bethe set of cells of the topological map.

In a first learning phase, the weights of the cells of the topologicalmap are initialized in a random fashion.

In a second learning phase, a search is made of the topological map tofind, for each trace portion E_(i) of the set E, the cell M_(i) nearestto E_(i). The weights of the M_(j) cells belonging to the neighborhoodof the M_(i) cell are then updated.

This phase is represented by the following equation:

    M.sub.j (t)=M.sub.j (t-1)+f ε(t), d, σ(t)!* E.sub.i (t)-M.sub.j (t-1)!

where:

     ε(t), d, σ(t)!=ε(t)*exp (=d.sup.2 /σ.sup.2 (t)!;

d is the distance between cell M_(i) and cell M_(j) ;

s(t) is the neighbor parameter;

e(t) is a gain factor.

According to another feature of the invention, e(t) is smaller than 1and preferably equal to about 0.7 on the first iteration where e(t) ands(t) decrease after each cycle of presentation of the trace portions oriteration. The iterations are considered to be completed when thedesired convergence is achieved, i.e., when a new presentation of theselected trace portions does not modify or only slightly modifies theordering of the cells.

When the learning process is complete, all the trace portions to beprocessed are presented on the topological map in order to classify themand to order them with respect to the classes defined in the topologicalmap.

Each trace portion presented on the topological map is assigned thenumber of the cell which corresponds to it, i.e., the cell having signalshape which is the nearest to the shape of the signal of said traceportion presented.

In a preferred embodiment of the invention, prior to the presentation ofall the trace portions each class or cell of the topological map isassigned a given color instead of a number. The fifteen cells numberedfrom 0 to 14 on the topological map in FIG. 2 can correspond to fifteendifferent colors, which range gradually, for example, from brown (class0) to purple (class 14). The different tones of any given color wouldsignifying that the corresponding classes are close to one another. FIG.2 also shows, on the right side thereof, a trace portion C to beclassified. If it is presented on the topological map, it is classifiedin cell 7 or, if necessary, in cell 6. It is noted that cell 6corresponds to a facies that is substantially similar to the faciesdefined by cell 7.

FIG. 3 schematically represents a map of seismic facies of thegeographical area or layer surveyed where each seismic faciescorresponds to one of the classes, 0 to 14, of the final topologicalmap. Numerals 100 to 114 correspond respectively to classes 0 to 14 ofthe topological map in FIG. 2. It may be observed that different classesare imbricated and/or are included in other classes.

I claim:
 1. A method for the automatic recognition of seismic facieswhich are at least one of between two horizons and about a horizon of agiven geological area, comprising the steps of:determining a givennumber of seismic facies to be recognized, taking a set of seismic traceportions concerning said area, defining a facies recognition parametercommon to all said trace portions and determining a value of saidparameter for each of said trace portions of said set, selecting traceportions from said set, choosing a one-dimensional neural networkcontaining as many cells as facies to be recognized, each cell beingassigned the value of the recognition parameter, making the neuralnetwork learn from the selected trace portions so that, when the step oflearning is complete, each cell corresponds to at least one of saidfacies to be recognized and said facies are gradually ordered,presenting each trace portion of said set to be processed to theclassified and ordered neural network, and assigning a number of thenearest cell to each of the trace portions presented to the network. 2.The method of claim 1, wherein said neural network is an unsupervisednetwork.
 3. The method of claim 2, wherein said unsupervised neuralnetwork is a one-dimensional Kohonen topological map.
 4. The method ofclaim 1, wherein said trace portions comprise the same number of samplesand said recognition parameter is defined by said sequence of samplescomprised between the two horizons or about said horizon.
 5. The methodof claim 1, wherein an overall recognition parameter is determined whichis common to all said trace portions.
 6. The method of claim 1, whereineach cell corresponds to a class which is assigned a color code, thedifferent colors being gradually ordered in a given range of colors witha slight variation in shade between any two consecutive colors of saidrange.
 7. The method of claim 1, wherein said recognized seismic faciesare represented on a map with their corresponding number.
 8. The methodof claim 6, wherein said recognized seismic facies are represented on amap with their corresponding color.
 9. A method for automaticallyclassifying seismic facies of a given geological area comprising thesteps of:determining a number of seismic facies to be classified;obtaining a set of seismic trace portions from said geological area;defining a seismic facies recognition parameter which is common to allsaid seismic trace portions; determining values of said seismic faciesrecognition parameter for each of said seismic trace portions of saidset; selecting a subset of trace portions from said set of seismic traceportions; obtaining a one-dimensional neural network having a number ofcells equal to said number of seismic facies to be classified, each cellbeing assigned a cell value corresponding to one of said values of saidseismic facies recognition parameter; automatically ordering said neuralnetwork cells such that differences between adjacent cell values in saidneural network are substantially minimized to obtain ordered cells;assigning a classification indicia to each cell of said ordered cells;comparing said values of said seismic facies recognition parameter ofsaid seismic trace portions of said set with said cell values of saidneural network; and assigning each of said seismic trace portions saidclassification indicia of said cell corresponding therewith.
 10. Themethod of claim 9, wherein said geological area comprises at leastseveral hundred seismic traces.
 11. The method of claim 9, wherein saidgeological area is bounded by two horizons.
 12. The method of claim 9,wherein said geological area is bounded by an area about an horizon ofsaid area.
 13. The method of claim 9, wherein the step of automaticallyordering said neural network comprises the steps of:assigning weights tosaid cells of said neural network in a random fashion; for each seismictrace portion, locating a cell of said neural network most nearlycorresponding thereto; and changing the weights of said cells near saidcell corresponding to said seismic trace portion.
 14. The method ofclaim 13, wherein said steps correspond to the following equation:

    M.sub.j (t)=M.sub.j (t-1)+f ε(t), d, σ(t)!* E.sub.i (t)-M.sub.j (t-1)!

where:

     ε(t), d, σ(t)!=ε(t)*exp (=d.sup.2 /σ.sup.2 (t)!;

d is the distance between cell M_(i) and cell M_(j) ; s(t) is theneighbor parameter; and ε(t) is a gain factor.
 15. The method of claim14, wherein ε(t) is less than
 1. 16. The method of claim 14, whereinε(t) is about 0.7.
 17. The method of claim 14, wherein the magnitudes ofε(t) and s(t) decrease for each iteration of locating said cellcorresponding to said seismic trace portions.